I lead a
machine learning group at Cornell where I advise
students in ORIE, Computer Science, Statistics, and CAM. You
can check out some of our
work here. I also teach
classes on Bayesian machine learning and information
theory. I organized the NIPS 2017 symposium on Interpretable
Machine Learning. I am interested in developing flexible,
interpretable, and scalable machine learning models, often
involving kernel learning, deep learning, and Gaussian
processes. I am particularly excited about probabilistic
approaches. My work has been applied to time series, vision,
NLP, spatial statistics, public policy, medicine, and physics.

Outside of work, I am a classical pianist who particularly
enjoys Glenn Gould's playing of Bach.